SOTAVerified

Intrusion Detection

Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.

Source: Machine Learning Techniques for Intrusion Detection

Papers

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Showing 711–720 of 800 papers

TitleStatusHype
IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection—0
Sequence Covering for Efficient Host-Based Intrusion DetectionCode0
Enhanced network anomaly detection based on deep neural networks—0
Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks—0
Active Learning for Wireless IoT Intrusion Detection—0
Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification SystemsCode0
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks—0
Enhancing Cohesion and Coherence of Fake Text to Improve Believability for Deceiving Cyber Attackers—0
V-CNN: When Convolutional Neural Network encounters Data Visualization—0
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection SystemsCode0
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13—Unverified
2K-Nearest NeighborsAccuracy (%)98.07—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9—Unverified